Multimodal Causal-Driven Representation Learning

• MCDRL is a multimodal learning framework that integrates structural causal modeling and representation learning to recover latent, causally relevant features.
• It employs methods like backdoor adjustment, bottleneck-plus-masking, and contrastive identification to disentangle causal features from spurious shortcuts.
• MCDRL enhances prediction robustness under distribution shifts and missing modalities, as validated across sentiment analysis, biomedicine, and imaging tasks.

Searching arXiv for papers on multimodal causal-driven representation learning and closely related multimodal causal representation learning. Multimodal Causal-Driven Representation Learning (MCDRL) denotes a family of multimodal learning formulations that combine structural causal modeling, causal intervention, and representation learning to recover latent factors or predictive features that are intended to be causally relevant rather than merely correlational. In the literature, MCDRL is used both as an explicit framework name and as an organizing concept for methods that debias multimodal predictors, disentangle causal from shortcut features, identify latent causal variables across partially shared modalities, and stabilize downstream inference under distribution shift, missing modalities, or fairness constraints (Liu et al., 2022, Jiang et al., 7 Aug 2025, Jiang et al., 26 Sep 2025).

1. Conceptual scope and problem setting

A common starting point is the observation that multimodal systems integrate heterogeneous signals such as text, audio, visual streams, clinical records, imaging, or biomedical measurements, and that standard multimodal objectives often exploit statistical shortcuts tied to modality-specific artifacts, annotation procedures, environment variables, or acquisition policies. In the 2022 review literature, MCDRL is defined as a paradigm that, given observed multi-modal variables such as visual inputs $X$, textual inputs $T$, audio inputs $A$, and an outcome $Y$, seeks to learn a shared representation $h(X,T,A,\dots)$ that captures only the invariant causal factors influencing $Y$, remains robust under $P' \neq P$, and supports causal intervention and counterfactual reasoning (Liu et al., 2022).

Subsequent work broadens this definition in several directions. In multimodal sentiment and language understanding, MCDRL is instantiated as causal debiasing of fused representations, typically by separating causal features from shortcut or spurious features and then applying backdoor-style intervention during training or inference (Jiang et al., 7 Aug 2025, Jiang et al., 26 Sep 2025). In biomedical and multimodal causal representation learning, the emphasis shifts toward identifiability: latent variables $Z$ generate modality-specific observations $X^{(m)}$, while also participating in an underlying causal graph, and the objective is to recover these latent components up to admissible transformations under explicit structural assumptions (Sun et al., 2024, Benhamza et al., 18 May 2026). Other strands replace a strict DAG-centric view with partial observability, partial latent sharing, or latent coupled variables, thereby treating multimodal contrastive learning itself as a causal-identification mechanism under specific assumptions (Yao et al., 2023, Liu et al., 2024).

This diversity implies that MCDRL is not a single model class. It is better understood as an umbrella for methods that use causal structure to constrain multimodal representation learning. Depending on the application, the central object may be a backdoor-adjusted predictor, an invariant subrepresentation, a component-wise identifiable latent code, a causal graph over discrete or continuous factors, or a fairness-aware embedding that removes the effect of sensitive or environment-specific variables (Zhu et al., 6 Sep 2025, Mai et al., 20 Apr 2026, Chen et al., 2023).

2. Structural causal formulations and target quantities

The foundational causal formulation in this literature is the structural causal model. A canonical review example posits an unobserved confounder $Z$ affecting both visual $T$0 and textual $T$1 inputs as well as the outcome $T$2, with

$T$3

and the corresponding backdoor adjustment

$T$4

This formalism motivates multimodal intervention, counterfactual generation, and module-wise factorization of multimodal predictors (Liu et al., 2022).

In supervised MCDRL for sentiment analysis, the same logic is specialized to disentangled latent variables. MMCI models text, audio, and visual inputs as a multi-relational graph $T$5 with $T$6 relation types, where $T$7 are intra-modal edges and $T$8 are inter-modal edges. Separate attention scores define causal and shortcut pathways, yielding $T$9 and $A$0, and Pearl’s backdoor adjustment is applied to the causal representation $A$1 against the confounding shortcut variable $A$2: $A$3 In practice, shortcut features are stratified through sampled perturbations and combined with $A$4 during training and test-time averaging (Jiang et al., 7 Aug 2025).

CaMIB adopts a different factorization. Each modality $A$5 is compressed by a per-modality information bottleneck, fused into $A$6, and split by a sigmoid mask into causal and shortcut subrepresentations: $A$7 An auxiliary variable $A$8, constructed by cross-modal token-level self-attention over $A$9, is used as an instrumental variable that is claimed to correlate with $Y$0 while remaining independent of $Y$1. Backdoor adjustment is approximated by recombining each sample’s $Y$2 with shortcut codes from other samples so that prediction becomes invariant to arbitrary $Y$3 (Jiang et al., 26 Sep 2025).

Medical MCDRL introduces additional confounding channels. For missing modalities, one formulation posits a latent confounder $Y$4, complete multimodal data $Y$5, missingness mask $Y$6, observed data $Y$7, causal features $Y$8, bias features $Y$9, and outcome $h(X,T,A,\dots)$0. The relevant backdoor-adjusted target is

$h(X,T,A,\dots)$1

which is approximated through prototype-based confounder dictionaries and deconfounding at the feature level (Zhu et al., 6 Sep 2025). Closely related work on multimodal clinical records under non-random modality missingness uses a latent health state $h(X,T,A,\dots)$2, modality missingness indicators $h(X,T,A,\dots)$3, and a rectifier for residual pattern-specific bias, explicitly treating modality observation patterns as part of the causal data-generating process (Liang et al., 21 Sep 2025).

A further line emphasizes environment-based invariance rather than explicit backdoor stratification. CmIR decomposes each modality $h(X,T,A,\dots)$4 into $h(X,T,A,\dots)$5 and $h(X,T,A,\dots)$6, with an environment variable $h(X,T,A,\dots)$7 affecting only the spurious branch. The target is a representation satisfying $h(X,T,A,\dots)$8 invariant across environments, enforced by invariance, mutual-information, and reconstruction constraints (Mai et al., 20 Apr 2026).

3. Architectural patterns and learning objectives

Despite domain differences, several architectural motifs recur across MCDRL.

The first motif is explicit causal–shortcut disentanglement. MMCI assigns each relation-specific edge a two-dimensional attention score $h(X,T,A,\dots)$9, normalizes it into $Y$0 and $Y$1, and propagates messages separately through causal and shortcut branches before summing across relations into $Y$2 and $Y$3. The training objective combines a supervised MSE loss on $Y$4, a KL loss driving shortcut predictions toward a uniform distribution, and an intervention MSE under backdoor stratification: $Y$5 At inference, randomly sampled shortcut features are added to $Y$6 and averaged, producing a Monte Carlo approximation of $Y$7 (Jiang et al., 7 Aug 2025).

The second motif is bottleneck-plus-masking. CaMIB first applies a variational information bottleneck to each modality,

$Y$8

implemented with VAE encoders. After fusion, a light MLP and sigmoid generate masks $Y$9 and $P' \neq P$0, producing $P' \neq P$1 and $P' \neq P$2. Training combines bottleneck regularization, causal prediction, shortcut uniformity, instrumental-variable alignment $P' \neq P$3, and intervention loss from recombined shortcut codes: $P' \neq P$4 This design treats compression, disentanglement, IV alignment, and backdoor-style intervention as a single MCDRL pipeline (Jiang et al., 26 Sep 2025).

The third motif is dual-stream or dual-branch decorrelation. In medical multimodal learning with missing modalities, CBDM uses two parallel GNN branches on the same patient–modality bipartite graph. An edge-wise gate produces $P' \neq P$5 and $P' \neq P$6, softly routing messages to causal and biased branches. The causal classifier is trained with standard cross-entropy, whereas the biased classifier uses generalized cross-entropy $P' \neq P$7, and counterfactuals are generated by pairing one patient’s $P' \neq P$8 with another patient’s $P' \neq P$9 (Zhu et al., 6 Sep 2025). A related dual-stream feature decorrelation framework duplicates a multimodal encoder into “causal” and “bias” streams, combines cross-entropy, generalized cross-entropy, and MINE-based mutual-information minimization, and is explicitly described as model-agnostic (Zhu et al., 29 Jan 2026).

The fourth motif is multimodal contrastive or alignment-based identification. Under partial observability, a single encoder per view and a contrastive-plus-entropy objective are used to block-identify the content variables shared by a subset of views, with selector variables enabling simultaneous recovery of multiple content blocks (Yao et al., 2023). In the latent partial causal model, symmetric InfoNCE over paired modalities is shown, in the infinite-sample limit, to recover coupled latents up to orthogonal or permutation-like transformations under the stated assumptions (Liu et al., 2024).

The fifth motif is multimodal causal generative modeling. causalPIMA combines uni-modal encoders, Product-of-Experts fusion, modality-specific decoders, a differentiable DAG parameterization based on $Z$0 and $Z$1, and a causal Gaussian mixture prior whose mixture components correspond to joint outcomes of discrete causal nodes. The objective is a single tractable ELBO, and acyclicity is enforced by construction rather than by an auxiliary penalty (Walker et al., 2023).

4. Identifiability, sufficiency, necessity, and causal completeness

A major strand of MCDRL is not primarily predictive; it is identifiable latent recovery. In multimodal biomedical observations, latent groups $Z$2 generate each modality $Z$3 through smooth nonlinear mixing together with modality-specific style variables $Z$4, while the full latent vector $Z$5 follows a nonparametric structural causal model. Under smoothness, invertibility, and minimal inter-modality connectivity, Theorem 1 yields subspace identifiability for each modality. With an additional combinatorial sparsity condition on the cross-block Jacobian, Theorem 2 establishes coordinate-wise identifiability up to univariate invertible transformations and permutation. The paper emphasizes structural sparsity of causal connections between modalities as the key fingerprint enabling identification (Sun et al., 2024).

This line is extended to partial latent sharing. In the 2026 formulation, each modality $Z$6 depends only on a subset $Z$7 of latent causal variables, allowing arbitrary overlap across modalities. Under injective mixing, properness, conditional or marginal independence, non-overlap, and sparsity assumptions, block-wise identifiability of shared variables and component-wise identifiability of all latents are established, together with recovery of the DAG up to permutation. Algorithmically, this is instantiated by per-modality autoencoders, a Wasserstein-based soft permutation module for shared-latent alignment, and a causal autoregressive normalizing flow with sparsity and acyclicity penalties (Benhamza et al., 18 May 2026).

A distinct identifiability route arises under partial observability. If each view $Z$8 is an invertible mixing of only a subset of latent variables, then the content variables shared by any subset of views can be block-identified by minimizing a contrastive-plus-entropy objective. The same work introduces “identifiability algebra,” comprising intersection, complement, and union rules for deriving additional identifiable blocks from known ones under stated independence assumptions (Yao et al., 2023).

Not all identifiable multimodal causal models are DAG-based. A latent partial causal model replaces a single shared latent node with coupled latents $Z$9 and $X^{(m)}$0 connected by an undirected edge. Under invertible generative maps and specific distributional assumptions on the coupled latent variables, symmetric contrastive learning identifies the shared structure up to an orthogonal linear map on the hypersphere or a scaled permutation plus shift on a convex body (Liu et al., 2024). This directly challenges the simplification that multimodal causal representation learning is necessarily equivalent to latent DAG recovery.

Another theoretical axis concerns whether learned representations are not only causal but also causally complete. One paper imports Pearl’s probability of necessity and sufficiency (PNS) into multimodal decomposition. For unimodal features, under exogeneity and monotonicity,

$X^{(m)}$1

In the multimodal setting, the method decomposes representations into modality-invariant and modality-specific blocks and derives tractable PNS-oriented losses for each, including cross-modality constraints for the specific block (Chen et al., 2024). A related framework introduces the Causal Complete Cause $X^{(m)}$2,

$X^{(m)}$3

and proposes a twin-network estimator with sufficiency and necessity branches, together with exogeneity- and monotonicity-oriented regularizers. This suggests an enlarged notion of MCDRL in which causal validity is measured not only by invariance but also by necessity and sufficiency of the learned features (Wang et al., 2024).

5. Empirical domains and representative results

The empirical literature shows that MCDRL is not confined to one benchmark family. It spans affective computing, healthcare, medical imaging, recommendation, and scientific data analysis.

Domain	Representative systems and results	Main causal mechanism
Multimodal sentiment, humor, sarcasm	CaMIB reaches Acc7$X^{(m)}$4, Acc2$X^{(m)}$5, F1$X^{(m)}$6, MAE$X^{(m)}$7, Corr$X^{(m)}$8 on CMU-MOSI; on the OOD CMU-MOSI split it attains Acc7$X^{(m)}$9, Acc2$Z$0, F1$Z$1; CmIR reports CMU-MOSI Acc2/MAE $Z$2, OOD Acc2 $Z$3, UR-FUNNY $Z$4, MUStARD $Z$5 (Jiang et al., 26 Sep 2025, Mai et al., 20 Apr 2026)	Backdoor intervention, IV alignment, invariant–spurious disentanglement
Medical multimodal prediction with missing data	GRAPE+MCDRL improves MIMIC-IV mortality AUC-PRC by $Z$6; MUSE+MCDRL improves eICU mortality AUC-PRC by $Z$7; CRL-MMNAR reaches MIMIC-IV ICU admission AUC $Z$8 and eICU readmission AUC $Z$9 (Zhu et al., 6 Sep 2025, Liang et al., 21 Sep 2025)	Missingness deconfounding, counterfactual disentanglement, MMNAR-aware fusion
Medical image segmentation	With a ViT-L/14 backbone, MCDRL reports average mDice $T$00 versus $T$01 for BiomedCoOp and $T$02 for CLIP-only; ResNet-50 CLIP reports $T$03 versus $T$04 and $T$05 (Liang et al., 7 Aug 2025)	Text-driven confounder dictionary and cross-attention intervention
Biomedical and scientific latent discovery	Numerical simulations achieve near-perfect factor recovery $T$06 and zero Structural Hamming Distance; synthetic multimodal numerical experiments report $T$07 in the 2-modal setting; causalPIMA recovers known latent factors and causal edges in synthetic circles and 3D-printed lattices (Sun et al., 2024, Benhamza et al., 18 May 2026, Walker et al., 2023)	Identifiability under sparsity, shared-latent alignment, differentiable DAG learning

In multimodal language understanding, the dominant testbed is sentiment analysis, frequently expanded to humor and sarcasm detection. CaMIB reports best or second-best performance on CMU-MOSEI with $T$08, $T$09, $T$10, $T$11, and $T$12, while ablations show that removing IB filtering, the IV constraint, the uniformity penalty, or backdoor intervention harms OOD performance by $T$13–$T$14 points (Jiang et al., 26 Sep 2025). MMCI is reported to improve several standard MSA datasets and OOD test sets by suppressing intra- and inter-modal biases through multi-relational causal intervention (Jiang et al., 7 Aug 2025). CmIR further supports the claim that separating invariant from environment-specific representations improves both OOD and noisy-modality robustness (Mai et al., 20 Apr 2026).

In healthcare, two closely related but distinct problems recur. One is missing modalities caused by non-random acquisition. A medical MCDRL framework models both missingness bias and distribution bias, and reports statistically significant improvements across MIMIC-IV, eICU, ADNI, and an in-hospital AFib dataset, together with improved embedding robustness measured by Euclidean distance between full and $T$15 masked inputs (Zhu et al., 6 Sep 2025). The other is modality missing-not-at-random in clinical records. CRL-MMNAR conditions fusion on missingness patterns, reconstructs modalities contrastively, and adds a cross-fitted rectifier; it reports up to $T$16 AUC improvement for hospital readmission and $T$17 for ICU admission on MIMIC-IV and eICU (Liang et al., 21 Sep 2025).

Medical image segmentation introduces a different multimodal setting: CLIP-like vision–language alignment is used to construct candidate lesion regions and a text-prompted confounder dictionary, after which a causal intervention network applies cross-attention to suppress domain-specific variations while preserving anatomical information. Reported gains include average mDice improvements with both ViT-L/14 and ResNet-50 backbones and lesion-type gains of $T$18 to $T$19 over BiomedCoOp (Liang et al., 7 Aug 2025).

In biomedicine and scientific discovery, the central empirical claim is less about prediction and more about faithful latent recovery. The biomedical multimodal CRL framework reports human-phenotype latent factors whose PC-inferred causal edges agree with known biomedical relationships such as sleep factor $T$20 blood-oxygen saturation and fundus factor $T$21 age (Sun et al., 2024). causalPIMA shows similar ambitions in a fully unsupervised multimodal VAE setting, where discrete causal nodes and a learned DAG organize mixture components in the latent space (Walker et al., 2023).

6. Misconceptions, limitations, and open directions

One common misconception is that MCDRL is reducible to backdoor adjustment. Backdoor intervention is central in MMCI, CaMIB, medical deconfounding under missing modalities, and medical image segmentation (Jiang et al., 7 Aug 2025, Jiang et al., 26 Sep 2025, Zhu et al., 6 Sep 2025, Liang et al., 7 Aug 2025). However, the broader literature also includes front-door and counterfactual perspectives in the review taxonomy, PNS-based losses, $T$22 risk, instrumental variables, invariant-risk formulations, identifiability algebra, differentiable DAG learning, and partial causal models beyond DAGs (Liu et al., 2022, Chen et al., 2024, Wang et al., 2024, Liu et al., 2024, Walker et al., 2023). This suggests that “causal-driven” in MCDRL names a broader design principle rather than a single adjustment formula.

A second misconception is that multimodal causal learning presupposes fully shared latent structure. Partial observability and partial latent sharing are central in several identifiability papers. One framework explicitly allows each view to observe only a subset $T$23 of latent variables and derives which intersections, complements, and unions are identifiable (Yao et al., 2023). Another permits arbitrary overlaps $T$24 across modalities and treats modality-specific and shared latents within the same identifiable model (Benhamza et al., 18 May 2026). In biomedical multimodal CRL, structural sparsity of cross-modal causal links is presented as the natural condition supporting detailed coordinate-wise identification (Sun et al., 2024).

The limitations are correspondingly heterogeneous. Some are assumption-level: exogeneity and monotonicity are required by PNS- and $T$25-style analyses, although one of these works explicitly seeks to relax them through instrumental variables and counterfactual modeling (Chen et al., 2024, Wang et al., 2024). Some are model-level: the biomedical identifiability framework currently assumes prior knowledge of each modality’s latent dimensionality, and overly dense cross-links breach its sparsity condition (Sun et al., 2024). Some are approximation-level: prototype dictionaries must sufficiently approximate $T$26 in missingness deconfounding, and performance gains diminish when missing rate exceeds $T$27 in the medical missing-modality setting (Zhu et al., 6 Sep 2025). Some are environment-level: simulated environments based on additive noise are acknowledged as a proxy for real distribution shifts in CmIR (Mai et al., 20 Apr 2026). The 2022 review also emphasizes SCM simplicity, imprecise confounder approximation, counterfactual synthesis quality, benchmark scarcity, and computational overhead as open obstacles for causal multimodal learning (Liu et al., 2022).

Open directions in the literature are relatively consistent. Several papers call for nonlinear and deep-generative multimodal mixing with retained identifiability, scalable methods for many modalities and large datasets, temporal or perturbational extensions, and better use of multimodal interventions in scientific and biomedical settings (Uhler et al., 6 Nov 2025, Benhamza et al., 18 May 2026, Liu et al., 2022). Others point toward active intervention design, richer confounder models, continuous or structured environments, and extensions from supervised to self-supervised or unsupervised multimodal causal learning (Uhler et al., 6 Nov 2025, Wang et al., 2024, Mai et al., 20 Apr 2026). A plausible implication is that the next phase of MCDRL will be judged less by isolated in-domain gains than by whether it can jointly provide identifiable latent structure, interventionally stable prediction, and practical scalability across heterogeneous multimodal regimes.